Systems and Methods for Determining a Status of a Component of a Device

ABSTRACT

Methods and systems for determining a status of a component of a device are provided. An example method includes triggering an action of a component of a device, and responsively receiving information associated with the action of the component from a sensor. The method further includes a computing system having a processor and a memory comparing the information with calibration data and determining a status of the component based on the comparison. In some examples, the calibration data may include information derived from data received from a pool of one or more devices utilizing same or similar components as the component. The determined status may include information associated with a performance of the component with respect to performances of same or similar components of the pool of devices. In one example, the device may self-calibrate the component based on the status.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/595,865 filed on Feb. 7, 2012 and U.S. patent application Ser. No.13/757,808 filed on Feb. 3, 2013, the entirety of which are hereinincorporated by reference.

FIELD

This disclosure relates to robot cloud computing, and in examples, todetermining a status of a component of a robotic device in a robot-cloudinteraction.

BACKGROUND

Cloud computing refers to provision of computational resources via acomputer network. In a traditional model of computing, both data andsoftware are fully contained on a user's computer. In cloud computing,however, the user's computer may contain relatively little software ordata (perhaps a minimal operating system and web browser, for example),and may serve as a display terminal for processes occurring on a networkof computers. A common shorthand provided for a cloud computing service(or even an aggregation of existing cloud services) is “the cloud”.

Cloud computing has been referred to as “client-server computing”,however, there may be distinctions between general cloud computing andclient-server computing. For example, client-server computing mayinclude a distributed application structure that partitions tasks orworkloads between providers of a resource or service (e.g., servers),and service requesters (e.g., clients). Client-server computinggenerally involves a one-to-one relationship between the server and theclient, whereas cloud computing includes generic services that can beaccessed by generic clients (e.g., a one-to-one relationship orconnection may not be required). Thus, cloud computing generallyincludes client-server computing, and additional services andfunctionality.

Cloud computing may free users from certain hardware and softwareinstallation and maintenance tasks through use of simpler hardware onthe user's computer that accesses a vast network of computing resources(e.g., processors, hard drives, etc.). Sharing of resources may reducecost to individuals. Thus, any computer connected to the cloud may beconnected to the same pool of computing power, applications, and files.Users can store and access personal files such as music, pictures,videos, and bookmarks or play games or use productivity applications ona remote server rather than physically carrying around a storage medium,such as a DVD or thumb drive.

In one example, a client device may be a computing device with sensors,actuators, and other components. For example, the client device may be arobotic device including sensors, such as a gyroscope, optical sensor,biosensor, wireless sensor, etc., and actuators, such as motors, wheels,moveable arms, etc.

SUMMARY

This disclosure may disclose, inter alia, systems and methods fordetermining a status of a component of a robotic device in a robot-cloudinteraction.

In one example, a method is provided that comprises triggering an actionof a component of a robotic device, and responsively receivinginformation associated with the action of the component of the roboticdevice from a sensor. The method may further include a computing systemhaving a processor and a memory comparing the information withcalibration data. The calibration data may comprise information derivedfrom data received from a pool of one or more robotic devices utilizingsame or similar components. According to the method, the computingsystem may also determine a status of the component based on thecomparison. In some examples, the status may comprise informationassociated with a performance of the component.

Any of the methods described herein may be provided in a form ofinstructions stored on a non-transitory, computer readable medium, thatwhen executed by a computing device, cause the computing device toperform functions of the method. Further examples may also includearticles of manufacture including tangible computer-readable media thathave computer-readable instructions encoded thereon, and theinstructions may comprise instructions to perform functions of themethods described herein.

In another example, a computer-readable memory having stored thereoninstructions executable by a computing device to cause the computingdevice to perform functions is provided. The functions may comprisetriggering an action of a component of a robotic device, andresponsively receiving information associated with the component of therobotic device from a sensor. The functions may further includecomparing the information with calibration data. The calibration datamay comprise information derived from data received from a pool of oneor more robotic devices utilizing same or similar components. Thefunctions may also include determining a status of the component basedon the comparison. In some examples, the status may comprise informationassociated with a performance of the component.

The computer readable memory may include a non-transitory computerreadable medium, for example, such as computer-readable media thatstores data for short periods of time like register memory, processorcache and Random Access Memory (RAM). The computer readable memory mayalso include non-transitory media, such as secondary or persistent longterm storage, like read only memory (ROM), optical or magnetic disks,compact-disc read only memory (CD-ROM), for example. The computerreadable memory may also be any other volatile or non-volatile storagesystems. The computer readable memory may be considered a computerreadable storage medium, for example, or a tangible storage medium.

In addition, circuitry may be provided that is configured to performlogical functions in any processes or methods described herein.

In still further examples, any type of devices may be used or configuredto perform logical functions in any processes or methods describedherein.

In another example, a system is provided that comprises a robotic deviceand a computing component. The robotic device may be configured toreceive, via a network, information associated with instructions forperforming an action of a component of the robotic device. The roboticdevice may be further configured to transmit, via the network,information associated with the component that is received from asensor. The computing component may comprise a processor and a memorycoupled to the processor, and may be capable of communicating with apool of robotic devices over the network. The computing component may beconfigured to trigger the action of the component of the robotic device,and responsively receive information associated with the component ofthe robotic device from the sensor. The computing component may also beconfigured to compare the information with calibration data. Thecalibration data may include information derived from data received fromthe pool of robotic devices, and the pool of robotic devices may utilizesame or similar components. The computing component may be furtherconfigured to determine a status of the component based on thecomparison. In some examples, the status may comprise informationassociated with a performance of the component.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the figures and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is an example system for cloud-based computing.

FIGS. 2A-2C are example illustrations of robotic devices.

FIG. 3 illustrates an example of a conceptual robot-cloud interaction.

FIG. 4 is an example system in which robotic devices may interact withthe cloud and share information with other cloud computing devices.

FIG. 5 is a block diagram of an example method of determining a statusof a component of a robotic device.

FIG. 6 is a flow diagram illustrating an example method of calibrating acomponent of a robotic device.

FIG. 7 is another block diagram of an example method of determining astatus of a component of a robotic device.

FIG. 8 is an example conceptual illustration of a display of a status ofa component of a robotic device.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying figures, which form a part hereof. In the figures, similarsymbols typically identify similar components, unless context dictatesotherwise. The illustrative embodiments described in the detaileddescription, figures, and claims are not meant to be limiting. Otherembodiments may be utilized, and other changes may be made, withoutdeparting from the scope of the subject matter presented herein. It willbe readily understood that the aspects of the present disclosure, asgenerally described herein, and illustrated in the figures, can bearranged, substituted, combined, separated, and designed in a widevariety of different configurations, all of which are explicitlycontemplated herein.

This disclosure may disclose, inter alia, methods and systems fordetermining a status of a component of a robotic device in a robot-cloudinteraction. An example method includes triggering an action of acomponent of a robotic device over time. For example, the action may bea predetermined movement, action, or other function that activates oneor more components of the robotic device. In some examples, thecomponents may include sensors (e.g., a gyroscope, optical sensor,biosensor, wireless sensor, etc.) and/or actuators (e.g., motors,wheels, moveable arms, etc.) of the robotic device. However, the methodmay be applicable to any component of the robotic device.

In response to the action of the component, information associated withthe component of the robotic device may be responsively received from asensor of the robotic device. For example, in an instance in which thecomponent is an actuator, the sensor may be associated with an actuatorof the robotic device and receive information in response to movement ofthe actuator.

According to the method, the information may be compared withcalibration data that may include information derived from data receivedfrom a pool of robotic devices. The pool of robotic devices may includerobotic devices utilizing same or similar components as the component ofthe robotic device. As an example, the data may include historicaloperational data or real-time operational data collected from apopulation of robotic devices. Information may be derived from the datacollected from the population of robotic devices to form the calibrationdata. In one instance, a computing system having a processor and amemory may compare the received information associated with thecomponent of the robotic device with the calibration data. For example,the calibration data may include nominal data such as averages forvarious characteristics of the component, and the nominal data may becompared with the received information associated with the component.

In addition, a status of the component may be determined by thecomputing system based on the comparison. For example, the comparisonmay indicate a miscalibrated or malfunctioning component, and the statusmay include information identifying the miscalibration or malfunction ofthe component.

In one example, the method may be performed by the robotic device, andthe robotic device may further self-calibrate or auto-tune the componentbased on the status. For example, the robotic device may receive thecalibration data from a server in a cloud in order to perform thecomparison. The cloud may also be connected to multiple robotic devicesutilizing same or similar components as the component of the roboticdevice.

In other examples, the method may be performed by the server in thecloud. In one instance, the server may receive the informationassociated with the component, and may notify the robotic device, or anoperator of the robotic device, of the status of the component based onthe comparison.

Additionally, in one instance, the method may be performed in anon-going basis so as to calibrate or identify a status of the componentin light of changing conditions. Thus, the method may provide for anautomated monitoring of components of robotic devices by way of arobot-cloud interaction.

Referring now to the figures, FIG. 1 is an example system 100 forcloud-based computing. Cloud-based computing generally refers tonetworked computer architectures in which application execution andstorage may be divided, to some extent, between client and serverdevices. A “cloud” may refer to a service or a group of servicesaccessible over a network (e.g., Internet) by client and server devices,for example.

In one example, any computer connected to the cloud may be connected tothe same pool of computing power, applications, and files. Thus, cloudcomputing enables a shared pool of configurable computing resources(e.g., networks, servers, storage, applications, and services) that canbe provisioned and released with minimal management effort or serviceprovider interaction. Users can store and access personal files such asmusic, pictures, videos, and bookmarks or play games or use productivityapplications on a remote server rather than physically carrying around astorage medium.

As an example, in contrast to a predominately client-based orserver-based application, a cloud-based application may store copies ofdata and/or executable program logic at remote server devices, whileallowing client devices to download at least some of this data andprogram logic as needed for execution at the client devices. In someexamples, downloaded data and program logic can be tailored tocapabilities of specific client devices (e.g., a personal computer,tablet, or mobile phone, or robot) accessing the cloud basedapplication. In addition, dividing application execution and storagebetween the client and server devices allows more processing to beperformed by the server devices taking advantage of server devicesprocessing power and capability, for example.

Cloud-based computing can also refer to distributed computingarchitectures in which data and program logic for a cloud-basedapplication are shared between one or more client devices and/or serverdevices on a near real-time basis. Parts of this data and program logicmay be dynamically delivered, as needed or otherwise, to various clientsaccessing the cloud-based application. Details of the architecture maybe transparent to users of client devices. Thus, a PC user or robotclient device accessing a cloud-based application may not be aware thatthe PC or robot downloads program logic and/or data from the serverdevices, or that the PC or robot offloads processing or storagefunctions to the server devices, for example.

In FIG. 1, a cloud 102 includes a cloud service 104, a cloud platform106, a cloud infrastructure 108, and a database 110. The cloud 102 mayinclude more or fewer components, and each of the cloud service 104, thecloud platform 106, the cloud infrastructure 108, and the database 110may comprise multiple elements as well. Thus, one or more of thedescribed functions of the system 100 may be divided up into additionalfunctional or physical components, or combined into fewer functional orphysical components. In some further examples, additional functionaland/or physical components may be added to the examples illustrated byFIG. 1. Delivery of cloud computing may involve multiple cloudcomponents communicating with each other over application programminginterfaces, such as web services and three-tier architectures, forexample.

The cloud 102 may represent a networked computer architecture, and inone example, the cloud service 104 represents a queue for handlingrequests from client devices. The cloud platform 106 may include afrontend of the cloud and may be coupled to the cloud service 104 toperform functions to interact with client devices. The cloud platform106 may include applications used to access the cloud 102 via a userinterface, such as a web browser. The cloud infrastructure 108 mayinclude service application of billing components of the cloud 102, andthus, may interact with the cloud service 104. The database 110 mayrepresent storage capabilities by the cloud 102, and thus, may beaccessed by any of the cloud service 104, the cloud platform 106, and/orthe cloud infrastructure 108.

The system 100 includes a number of client devices coupled to orconfigured to be capable of communicating with components of the cloud102. For example, a computer 112, a mobile device 114, a host 116, and arobot client 118 are shown coupled to the cloud 102. Of course, more orfewer client devices may be coupled to the cloud 102. In addition,different types of client devices may be coupled to the cloud 102. Forexample, any of the client devices may generally comprise a displaysystem, memory, and a processor.

The computer 112 may be any type of computing device (e.g., PC, laptopcomputer, etc.), and the mobile device 114 may be any type of mobilecomputing device (e.g., laptop, mobile telephone, cellular telephone,etc.).

The host 116 may be any type of computing device or transmitterincluding a laptop computer, a mobile telephone, etc., that isconfigured to transmit data to the cloud 102.

The robot client 118 may comprise any computing device that hasconnection abilities to the cloud 102 and that has an actuationcapability (e.g., electromechanical capabilities). A robot may furtherbe a combination of computing devices. In some examples, the robot 118may collect data and upload the data to the cloud 102. The cloud 102 maybe configured to perform calculations or analysis on the data and returnprocessed data to the robot client 118. In some examples, as shown inFIG. 1, the cloud 102 may include a computer that is not co-located withthe robot client 118. In other examples, the robot client 118 may senddata to a second client (e.g., computer 112) for processing.

Any of the client devices may include additional components. Forexample, the robot client 118 may include one or more sensors, such as agyroscope or an accelerometer to measure movement of the robot client118. Other sensors may further include any of Global Positioning System(GPS) receivers, encoders, infrared sensors, optical sensors,biosensors, Radio Frequency identification (RFID) systems, wirelesssensors, and/or compasses, among others, for example.

In addition, any of the client devices may include an integrateduser-interface (UI) that allows a user to interact with the device. Forexample, the robot client 118 may include various buttons and/or atouchscreen interface that allow a user to provide input. As anotherexample, the robot client device 118 may include a microphone configuredto receive voice commands from a user. Furthermore, the robot client 118may include one or more interfaces that allow various types ofuser-interface devices to be connected to the robot client 118.

In FIG. 1, communication links between client devices and the cloud 102may include wired connections, such as a serial or parallel bus.Communication links may also be wireless links, such as link 120, whichmay include Bluetooth, IEEE 802.11 (IEEE 802.11 may refer to IEEE802.11-2007, IEEE 802.11n-2009, or any other IEEE 802.11 revision), orother wireless based communication links.

In other examples, the system 100 may include access points throughwhich the client devices may communicate with the cloud 102. Accesspoints may take various forms, for example, an access point may take theform of a wireless access point (WAP) or wireless router. As anotherexample, if a client device connects using a cellular air-interfaceprotocol, such as a CDMA or GSM protocol, an access point may be a basestation in a cellular network that provides Internet connectivity viathe cellular network.

As such, the client devices may include a wired or wireless networkinterface through which the client devices can connect to the cloud 102(or access points). As an example, the client devices may be configureduse one or more protocols such as 802.11, 802.16 (WiMAX), LTE, GSM,GPRS, CDMA, EV-DO, and/or HSPDA, among others. Furthermore, the clientdevices may be configured use multiple wired and/or wireless protocols,such as “3G” or “4G” data connectivity using a cellular communicationprotocol (e.g., CDMA, GSM, or WiMAX, as well as for “WiFi” connectivityusing 802.11). Other examples are also possible.

FIGS. 2A-2C are example illustrations of robotic devices. Any of therobots illustrated in FIGS. 2A-2C may be configured to operate accordingto example methods described herein, or according to instructionsreceived from devices that may be configured to operate according toexample methods described herein.

An example illustration of a robotic device 200 is shown in FIG. 2A. Inone example, the robotic device 200 is configured as a robot. In someexamples, a robot may contain computer hardware, such as a processor202, memory or data storage 204, and one or more sensors 206. Forexample, a robot controller (e.g., processor 202, computing system, andsensors 206) may all be custom designed for a specific robot. The robotmay have a link to access cloud servers (as shown in FIG. 1). A wiredlink may include, for example, a parallel bus or a serial bus such as aUniversal Serial Bus (USB). A wireless link may include, for example,Bluetooth, IEEE 802.11, Cellular (such as GSM, CDMA, UMTS, EV-DO, WiMAX,or LTE), or Zigbee, among other possibilities.

In one example, the storage 204 may be used for compiling data fromvarious sensors 206 of the robotic device 200 and storing programinstructions. The processor 202 may be coupled to the storage 204 andmay be configured to control the robotic device 200 based on the programinstructions. The processor 202 may also be able to interpret data fromthe various sensors 206 on the robot. Example sensors may include acamera, smoke sensors, light sensors, radio sensors, infrared sensors,microphones, speakers, gyroscope, accelerometer, distance sensors,encoders, a camera, radar, capacitive sensors and touch sensors, etc.Example distance sensors include infrared ranging sensors, photoelectricdistance sensors, proximity sensors, ultrasonic sensors, radar, or othertypes of sensors that may provide outputs used to determine a distanceof the robotic device 200 to an object.

The robotic device 200 may also have components or devices that allowthe robotic device 200 to interact with an environment of the roboticdevice 200. For example, the robotic device 200 may have a camera toprovide images of a field of view of the environment as well asmechanical actuators 208, such as motors, wheels, movable arms, etc.,that enable the robotic device 200 to move or interact with theenvironment.

In some examples, various sensors and devices on the robotic device 200may be modules. Different modules may be added or removed from therobotic device 200 depending on requirements. For example, in a lowpower situation, a robot may have fewer modules to reduce power usages.However, additional sensors may be added as needed. To increase anamount of data a robot may be able to collect, additional sensors may beadded, for example.

In some examples, the robotic device 200 may be configured to receive adevice, such as device 210, that includes the processor 202, the storage204, and the sensors 206. For example, the robotic device 200 may be arobot that has a number of mechanical actuators (e.g., a movable base),and the robot may be configured to receive a mobile telephone tofunction as the “brains” or control components of the robot. The device210 may be considered a module of the robot. The device 210 may bephysically attached to the robot or in communication with the robot. Forexample, a mobile phone may sit on a robot's “chest” and form aninteractive display. The device 210 may provide a robot with sensors, awireless link, and processing capabilities, for example. The device 210may allow a user to download new routines for his or her robot from thecloud. For example, a laundry folding routine may be stored on thecloud, and a user may be able to select this routine using a mobilephone to download the routine from the cloud. When the mobile phone isplaced into or coupled to the robot, the robot would be able to performthe downloaded action.

In some examples, the robotic device 200 may be coupled to a mobile orcellular telephone to provide additional sensing capabilities. Thecellular phone may not be physically attached to the robot, but may becoupled to the robot wirelessly. For example, a low cost robot may omita direct connection to the internet. This robot may be able to connectto a user's cellular phone via a wireless technology (e.g., Bluetooth)to be able to access the internet. The robot may be able to accessvarious sensors and communication means of the cellular phone. The robotmay not need as many sensors to be physically provided on the robot,however, the robot may be able to keep the same or similarfunctionality.

Thus, the robotic device 200 may include mechanical robot features, andmay be configured to receive the device 210 (e.g., a mobile phone),which can provide additional peripheral components to the robotic device200, such as any of an accelerometer, gyroscope, compass, GPS, camera,WiFi connection, a touch screen, etc., that are included within thedevice 210.

FIG. 2B illustrates a graphical example of a robot 212. In FIG. 2B, therobot 212 is shown as a mechanical form of a person including arms,legs, and a head. The robot 212 may be configured to receive any numberof modules or components, such a mobile phone, which may be configuredto operate the robot. In this example, a device (e.g., robot 212) can beattached to a mobile phone (e.g., device 210) to provide the mechanicalrobot 212 with functionality enabling the robot 212 to communicate withthe cloud to cause operation/functions of the robot 212. Other types ofdevices that have connectivity to the Internet can be coupled to robot212 to provide additional functions on the robot 212. Thus, the device210 may be separate from the robot 212 and can be attached or coupled tothe robot 212.

In one example, the robot 212 may be a toy with only limited mechanicalfunctionality, and by connecting the device 210 to the robot 212, thetoy robot 212 may now be capable of performing a number of functionswith the aid of the device 210 and/or the cloud. In this manner, therobot 212 (or components of a robot) can be attached to a mobile phoneto transform the mobile phone into a robot (e.g., with legs/arms) thatis connected to a server to cause operation/functions of the robot.

FIG. 2C illustrates another example of a robot 214. The robot 214includes a computing device 216, sensors 218, and a mechanical actuator220. In this example, the computing device 216 may be a laptop computer,which may be coupled to the sensors 218. The sensors 218 may include acamera, infrared projectors, and other motion sensing or vision sensingelements. The sensors 218 may be included within a tablet device, whichmay also function as the computing device 216. The mechanical actuator220 may include a base, wheels, and a motor upon which the computingdevice 216 and the sensors 218 can be positioned, for example.

Any of the robots illustrated in FIGS. 2A-2C may be configured tooperate according to a robot operating system (e.g., an operating systemdesigned for specific functions of the robot). A robot operating systemmay provide libraries and tools (e.g., hardware abstraction, devicedrivers, visualizers, message-passing, package management, etc.) toenable robot applications. Examples of robot operating systems includeopen source software such as ROS (robot operating system), DROS, orARCOS (advanced robotics control operating system); as well asproprietary software, and other examples also include ROSJAVA. A robotoperating system may include publish and subscribe functionality, andmay also include functionality to control components of the robot, suchas head tracking, base movement (e.g., velocity control, navigationframework), etc.

FIG. 3 illustrates an example of a conceptual robot-cloud interaction. Arobot, such as a robot described and illustrated in FIG. 2, may connectto a network of computers (e.g., the cloud), and may request data orprocessing to be performed by the cloud. In one example, the robot mayinclude a number of sensors and mechanical actuators that may generallyprovide motor control for the robot. Outputs of the sensors, such ascamera feeds, vision sensors, etc., may be provided to the cloud, whichcan process the outputs to enable the robot to perform functions. Thecloud may process a camera feed, for example, to determine a location ofa robot, perform object recognition, or to indicate a navigation pathwayfor the robot.

FIG. 3 generally illustrates motor controllers in which each module mayconceptually represent a computer or node on the cloud that performsprocessing using motor controller inputs or data from the robot. FIG. 3also generally illustrates sensors in which each module may conceptuallyrepresent a computer or node on the cloud that performs processing usingsensor inputs or data from the robot. FIG. 3 further generallyillustrates applications in which each module may conceptually representa computer or node on the cloud that performs specific functions of anumber of applications, e.g., navigation application, mappingapplication, etc. In addition, FIG. 3 further generally illustratesplanning in which each module may conceptually represent a computer ornode on the cloud that performs processing for the robot, such asgeneral planning or computing processing.

As shown, any of the modules may be interconnected, and/or maycommunicate to receive data or instructions from each other so as toprovide a specific output or functionality for the robot.

In one example, the robot may send data to a cloud for data processing,and in another example, the robot may receive data from the cloud. Thedata received from the cloud may be in many different forms. Thereceived data may be a processed form of data the robot sent to thecloud. The received data may also come from sources other than therobot. For example, the cloud may have access to other sensors, otherrobots, and the internet.

FIG. 4 is an example system 400 in which robots may interact with thecloud and share information with other cloud computing devices. Thesystem 400 illustrates robots 402, 404, 406, and 408 (e.g., asconceptual graphical representations) each coupled to a cloud 410. Eachrobot 402, 404, 406, and 408 may interact with the cloud 410, and mayfurther interact with each other through the cloud 410, or through otheraccess points and possibly directly (e.g., as shown between robots 406and 408).

The cloud 410 may receive input from several robots. Data from eachrobot may be complied into a larger data set. For example, the robot 402may take a picture of an object and upload the picture to the cloud 410.An object recognition program in the cloud 410 may be configured toidentify the object in the picture and provide data to all the robotsconnected to the cloud 410 about the recognized object, as well aspossibly about other characteristics (e.g., metadata) of the recognizedobject, such as a location, size, weight, color, etc. Thus, every robotmay be able to know attributes of an object in a photo uploaded by therobot 402.

The robots 402, 404, 406 and 408 may perform any number of actions withan area, people, other robots, etc. In one example, each robot 402, 404,406 and 408 has WiFi or other network based connectivity and willupload/publish data to the cloud 410 that can then be shared with anyother robot. In this manner, each robot 402, 404, 406 and 408 sharesexperiences with each other to enable learned behaviors. Each robot 402,404, 406, and 408 will have access to real-time, up-to-date data.Overall, the robots 402, 404, 406, and 408 may be configured to sharedata that is collected to enable faster adaptation, such that each robot402, 404, 406, and 408 can build upon a learned experience of a previousrobot.

The database 412 may be accessible by all robots through the cloud 410(or alternatively directly accessible by all robots withoutcommunication through the cloud 410). The database 412 may thus be ashared knowledge base stored in the cloud 410. In some examples, robotsmay share learned behaviors through the cloud 410. The cloud 410 mayhave a server that stores robot-learned activities or behaviorsresulting in a shared knowledge base of behaviors and heuristics forobject interactions (e.g., a robot “app store”).

Thus, within examples, the robots 402, 404, 406, and 408 may shareinformation through the cloud 410, and may access the database 412. Therobots 402, 404, 406, and 408 may access the cloud 410 to perform anynumber of functions or methods described herein.

In one example, a robot may interact with the cloud to determine thestatus of a component. For example, the cloud may receive informationfrom multiple robots including logs of statuses and user interactions.In some examples, automated processes within the cloud or the robot maybe used to determine the status of the component.

FIG. 5 is a block diagram of an example method 500 of determining astatus of a component of a robotic device. Method 500 shown in FIG. 5presents an embodiment of a method that could be used with the system100, for example, and may be performed by a device, such as any devicesillustrated in FIGS. 1-2, or components of the devices. Method 500 mayinclude one or more operations, functions, or actions as illustrated byone or more of blocks 502-508. Although the blocks are illustrated in asequential order, these blocks may also be performed in parallel, and/orin a different order than those described herein. Also, the variousblocks may be combined into fewer blocks, divided into additionalblocks, and/or removed based upon the desired implementation.

In addition, for the method 500 and other processes and methodsdisclosed herein, the block diagram shows functionality and operation ofone possible implementation of present embodiments. In this regard, eachblock may represent a module, a segment, or a portion of program code,which includes one or more instructions executable by a processor orcomputing device for implementing specific logical functions or steps inthe process. The program code may be stored on any type of computerreadable medium, for example, such as a storage device including a diskor hard drive. The computer readable medium may include non-transitorycomputer readable medium, for example, such as computer-readable mediathat stores data for short periods of time like register memory,processor cache and Random Access Memory (RAM). The computer readablemedium may also include non-transitory media, such as secondary orpersistent long term storage, like read only memory (ROM), optical ormagnetic disks, compact-disc read only memory (CD-ROM), for example. Thecomputer readable media may also be any other volatile or non-volatilestorage systems. The computer readable medium may be considered acomputer readable storage medium, for example, or a tangible storagedevice.

In addition, for the method 500 and other processes and methodsdisclosed herein, each block in FIG. 5 may represent circuitry that iswired to perform the specific logical functions in the process.

At block 502, the method 500 includes triggering an action of acomponent of a robotic device. In one example, the component may be asensor receiving information from an environment in which the roboticdevice resides. In another example, the component may be a sensor on arobotic device monitoring performance of the robotic device or theenvironment. In yet another example, the component may be an actuator ofthe robotic device.

In one instance, the triggering of the action may occur over time inorder to identify a performance of the component over time. For example,the action of the component may be prompted at predetermined intervals.In other examples, the action of the component may be triggered by aserver monitoring a status of the component over time. In one example,the triggering of the action may occur upon initialization of therobotic device. The robotic device may perform the action when therobotic device is powered on, for example.

At block 504, the method 500 includes responsively receiving informationassociated with the action of the component of the robotic device. Insome examples, the information associated with the component of therobotic device may be received from a sensor of the robotic device. Inother examples, the information associated with the component may bereceived from a sensor that is not a part of the robotic device. Theinformation associated with the component may include a temperature,amount of light, amount of power draw (e.g., amount of current or amountof wattage), battery life, audio information (e.g., a decibel level oraudio clip), visual information (such as images/videos etc.), amongother possibilities.

In one instance, the robotic device may transmit information associatedwith the component at one time, or any combination of times, such as atime prior to the action of the component, a time during the action ofthe component, or a time subsequent to the action of the component. Inanother instance, the robotic device may continuously transmit theinformation associated with the component, while a database may recordinformation associated with the component at instances in timesurrounding or during the action of the component.

At block 506, the method 500 includes a computing system having aprocessor and a memory comparing the information with calibration datacomprising information derived from data received from a pool of roboticdevices utilizing same or similar components. The pool may include oneor more than one robotic devices. The computing system may have accessto the calibration data. In some examples, the calibration data may bederived from data received from other robotic devices utilizing the sameor a similar component as the component of the robotic device. Forexample, the computing system may be a computing module of the roboticdevice, another given robotic device of the pool of robotic devices, ora server separate from the robotic devices (e.g., a server in a cloudwith which the pool of robotic devices is connected via a network) thathas access to a database comprising the calibration data.

In one example, the calibration data may comprise information receivedfrom the robotic device in response to a previous action of thecomponent and/or historical information received from other roboticdevices of the pool of robotic devices. The comparison may involveperforming a regression test to identify regressions or changes in aperformance of the component due to a passage of time or changingconditions of an environment in which the robotic device resides,operates, or is configured to operate (e.g., changes in lighting,temperature, floor surface, etc.). For example, the action of thecomponent may include capturing an image of a predetermined object usinga camera of the robotic device. The image may be compared with otherimages of the object previously captured to identify regressions orerrors between the images.

In another example, the calibration data may include real-time data fromthe pool of robotic devices, and the calibration data may be modifiedcontinuously based on the real-time data. Thus, the calibration data maybe adjusted to account for changing conditions experienced by the poolof robotic devices.

In yet another example, the pool of robotic devices may include a fleetof similar robotic devices operating in an area with similar functions.The calibration data may include information received from the pool ofrobotic devices. In some examples, the comparison may include astatistical analysis of the information received from the similarrobotic devices to determine whether a performance component is withinan acceptable limit of performances of the same or similar components ofthe similar robotic devices.

At block 508, the method 500 includes determining a status of thecomponent based on the comparison. In some examples, the status mayinclude information associated with a performance of the component ofthe robotic device. For example, the status of the component may bedetermined relative to performances of same or similar componentsutilized by the pool of robotic devices.

Optionally, at block 510, the status may be sent to the robotic deviceor another entity. In another example, the status may be a flagindicating the component (or the robotic device) is prone to moreerrors. In one instance, based on the flag, an alert indicating thestatus may be generated and provided to another entity (e.g., anotherrobotic device, an operator of the pool of robotic devices, atechnician, etc.) based on the flag.

In an example in which an image received from an optical sensor of therobotic device includes regressions with respect to previous imageswithin the calibration data, the status of the component may indicatethat the optical sensor needs to be adjusted. For example, an object inthe image may have shifted or be out of focus, and the status mayindicate the optical sensor needs to be adjusted accordingly.

In some examples, the method 500 may be performed by the robotic device.For example, the robotic device may query a server for the calibrationdata, and receive from the server the calibration data. The calibrationdata may include nominal values or averages associated with componentsof the pool of robotic devices. A computing system of the robotic devicemay compare the information associated with the component with thecalibration data, and determine a status of the component. In oneinstance, identities of other robotic devices of the pool of roboticdevices may be secured in the server and not included within thecalibration data sent to the robotic device. For example, thecalibration data may indicate the pool of robotic devices includesanonymous robotic devices. Thus, the calibration data may, in someexamples, be anonymous data.

In other examples, the method 500 may be performed by a server (e.g., aserver in a cloud). The information received from the robotic device maybe stored in a database. The database may include historical and/orreal-time data from other robotic devices of the pool of roboticdevices. A computing system of the server in the cloud may receive theinformation associated with the component of the robotic device, comparethe information with the calibration data, and determine the status ofthe component. In one example, information associating an identity ofthe robotic device and the information associated with the component ofthe robotic device may be secured within the server. Thus, in someexamples, information associated with the identity of the robotic devicemay be private, and not be distributed to other robotic devices.

FIG. 6 is a flow diagram 600 illustrating an example method ofcalibrating a component of a robotic device 602. In some examples, therobotic device 602 may calibrate the component based on the status ofthe component.

For example, the robotic device 602 may determine a sensor value for asensor at block 604. In one instance, the sensor may be a device capableof determining a temperature. As an example, the robotic device 602 maydetermine a temperature value of seventy-six degrees.

Additionally, sensor values for same or similar components of a pool ofone or more robotic devices 606 may be determined at block 608. In oneexample, a population mean (e.g., seventy-eight degrees) may bedetermined based on sensor values from other sensors of the pool ofrobotic devices. However, in other examples, other statistical measures(e.g., median, mode, etc.) may be used for determining a nominal valuefor the sensor. In some instances, the information from the pool ofrobotic devices 606 may be real-time data. In other instances, theinformation from the pool of robotic devices 606 may be historical datastored in a database.

In one example, the pool of robotic devices 606 may be in a locationthat is the same location or near to a location of the robotic device602. Thus, the information from sensor values of the pool of roboticdevices 606, and the population mean, may be similar to data which maybe expected to be received from the robotic device 602.

At decision block 610, the population mean for the pool of roboticdevices 606 may be compared to the sensor value of the robotic device602. In one instance, an absolute value of a difference between thesensor value and the population mean may be compared to a threshold(e.g., a threshold of 1 degree). In other instances, the comparison maybe based on other calculations. Similarly, the threshold may be variedbased on historical information or real-time data associated with thepool of robotic devices 606 or as configured by a user or technician.

Based on the decision at block 610, when the absolute value of thedifference is greater than the threshold, the sensor of the roboticdevice 602 may be modified at block 612. For example, an offset of thesensor may be calibrated or modified such that the sensor value issubstantially consistent with the population mean of the pool of roboticdevices 606. In some examples, the modification of the offset may beperformed by the robotic device 602. In other examples, the modificationof the offset may be performed in a device that the robotic device 602is configured to communicate with via cloud computing. In one instance,the value of the offset may be the difference between the sensor valueand the population mean. In another instance, the offset may be a curveto account for sensors that may be miscalibrated at extremes (e.g., veryhigh or very low temperatures). In some examples, the sensor value andan amount of offset applied to the sensor may be stored in a database atblock 614.

Based on the decision at block 610, when the absolute value of thedifference is less than the threshold, no modification to the sensorvalue may be made, and the sensor value may be stored in the database atblock 614.

In an alternative embodiment (not shown), if it is determined that thesensor value is outside of a second threshold, a communication to a hostmay be triggered. The communication may indicate that a part replacementor a repair call is warranted, for example.

In some examples, rather than requesting a replacement sensor, therobotic device 602 may self-calibrate based on information from the poolof robotic devices 606. Additionally, the ability of the robotic device602 to self-calibrate may reduce an amount of manual tuning and/ordiagnostics required by a technician. The method 600 may also beapplicable to other components, such as motors, actuators, visionsystems, positioning systems, or other components of the robotic device602. Thus, the example is not meant to be limited to sensors.

FIG. 7 is another block diagram of an example method of determining astatus of a component of a robotic device. The method 700 shown in FIG.7 presents an embodiment of a method that may, for example, be used bythe system 100 of FIG. 1, and may be performed by a device, such as anydevices illustrated in FIGS. 1-2, or components of the devices Method700 may include one or more operations, functions, or actions asillustrated by one or more of blocks 702-710. Although the blocks areillustrated in a sequential order, these blocks may also be performed inparallel, and/or in a different order than those described herein. Also,the various blocks may be combined into fewer blocks, divided intoadditional blocks, and/or removed from the method, based upon thedesired implementation of the method. Each block may represent a module,a segment, or a portion of program code, which includes one or moreinstructions executable by a processor for implementing specific logicalfunctions or steps in the process. In addition, each block in FIG. 7 mayrepresent circuitry that is wired to perform the specific logicalfunctions in the process.

Initially, at block 702, the method 700 includes initialize a roboticdevice. In some examples, initialization may refer to a time when therobotic device is activated, a time when the robotic device is poweredon after having been powered down, or a time when the robotic device hasbeen initialized after maintenance has been performed on the roboticdevice. However, the method 700 may also be applicable to other timesand the example is not meant to be limiting.

At block 704, the method 700 includes perform a predetermined movementof an actuator of the robotic device. In one instance, the roboticdevice may make a few moves (e.g., move forward, right, backward, etc.)according to a predetermined schedule. In another instance, the movementmay involve exercising a movable arm associated with the robotic device.

At block 706, the method 700 includes receive information associatedwith a movement of the actuator. For example, a sensor associated withthe actuator may receive information during and/or around a time whenthen movement of the actuator occurs.

At block 708, the method 700 includes compare the information withcalibration data. In one example, the information may be compared with aset of calibration data on a server to identify if the robotic devicehas any problems (e.g., miscalibrations or malfunctions). The data onthe server may include historical operational data collected from apopulation of robotic devices such that the server may determinebaseline operational data. The baseline operational data may correspondto an expected average range of performance based on data collected frommultiple robotic devices. In one instance, if the robotic device movesto the right but does not traverse a certain distance over a certaintime period, a problem may be identified with a wheel.

At block 710, the method 700 includes determine a status of theactuator. For example, a server may identify any problems or notify auser of a malfunctioning robotic device. The status may be associatedwith performance of the component. In some examples, a display of thestatus may be provided.

FIG. 8 is an example conceptual illustration of a display 800 of astatus of a component of a robotic device. In one example, the display800 may include information associated with a pool of robotic devices.For example, as shown in the display 800, one or more percentile curves802 may indicate typical performance of a component versus a life of thecomponent as determined from calibration data. Additionally, theperformance of the robotic device may be shown by an individual curve804 relative to performances of components of the pool of roboticdevices.

In one example, the display 800 may also include component statistics806. The statistics 806 may be associated with performance of thecomponent of the robotic device. Optionally, the statistics may alsoinclude a graphical indicator illustrating a relationship of thecomponent to an average value (e.g., an upward-pointing triangle mayindicate the statistic is greater than the average value, adownward-pointing triangle may indicate the statistic is less than theaverage value, etc.). The average value may be determined based onreal-time data from the pool of robotic devices and/or historical dataassociated with the pool of robotic devices.

In some examples, the display 800 may be accessed via a web interfaceconnected to a server in a cloud. In other examples, the display 800 maybe provided on a display associated with the robotic device andconfigured to receive information from the server in the cloud. In oneinstance, the display 800 may further include information indicatingon-screen alerts based on the status of the component as determined bythe server in the cloud (not shown).

Although the systems and methods have been described with respect to agiven component of a robotic device, in some examples, the systems andmethods may provide information associated with a general health for aplurality of robotic devices (e.g., individual health information foreach of a fleet of network connected robotic devices). In one instance,a server may combine information associated with a performance of aplurality of components of a given robotic device with respect toperformance of the plurality of components by the fleet of roboticdevices to determine a general health of the given robotic device.

Additionally, although the systems and methods have been described withrespect to robotic devices, the systems and methods may also beapplicable to monitoring of any cloud connected devices (e.g., cars,phones, tablets, etc.). Given a fleet of similar devices publishinginformation associated with components of the devices to a cloud, acentral computing system in the cloud may be capable of monitoringstatus and health of the components or the devices based on historicallogs of data associated with the components/devices and/or real-timedata received from the devices.

It should be understood that arrangements described herein are forpurposes of example only. As such, those skilled in the art willappreciate that other arrangements and other elements (e.g. machines,interfaces, functions, orders, and groupings of functions, etc.) can beused instead, and some elements may be omitted altogether according tothe desired results. Further, many of the elements that are describedare functional entities that may be implemented as discrete ordistributed components or in conjunction with other components, in anysuitable combination and location.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims, along with the full scope ofequivalents to which such claims are entitled. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting.

What is claimed is:
 1. A method comprising: triggering, by one or morecomputing devices, an action of a component of a device; responsivelyreceiving, by the one or more computing devices, information associatedwith the action of the component of the device; comparing, by the one ormore computing devices, the received information with calibration datacomprising information derived from data received from a pool of one ormore devices utilizing same or similar components, wherein the datareceived from the pool comprises information associated with respectiveactions of respective components of the one or more devices of the pool;and determining, by the one or more computing devices, a status of thecomponent based on the comparison, wherein the status comprisesinformation associated with a performance of the component.
 2. Themethod of claim 1, wherein the calibration data further comprisesinformation responsively received and associated with the component ofthe device in response to a previous action of the component, andwherein comparing the information comprises performing a regressiontest.
 3. The method of claim 1, wherein the method is performed by thedevice, and the method further comprises: querying a server for thecalibration data; and receiving from the server the calibration data. 4.The method of claim 1, wherein the pool of devices includes anonymousdevices.
 5. The method of claim 4, wherein the information derived fromdata received from a pool of devices utilizing same or similarcomponents comprises real-time data from the pool of devices, and themethod further comprises: modifying the calibration data based on thereal-time data from the pool of devices.
 6. The method of claim 1,wherein the method is performed by a server.
 7. The method of claim 6,further comprising: storing the information associated with thecomponent of the device in a database in the server, wherein informationassociating an identity of the device and the information associatedwith the component is secured within the server.
 8. The method of claim1, further comprising: the device calibrating the component based on thestatus of the component.
 9. The method of claim 8, wherein the componentis a sensor, and wherein calibrating the component comprises modifyingan offset of the component such that the information associated with thecomponent is substantially consistent with a population mean, whereinthe population mean is derived from the data received from the pool ofdevices.
 10. The method of claim 8, wherein the method is performed on acontinuous basis so as to calibrate the component based on changingconditions in an environment in which the device resides.
 11. The methodof claim 1, wherein the method is performed upon initialization of thedevice.
 12. The method of claim 1, further comprising: providing adisplay of the status of the component, wherein the display comprisesinformation associated with the pool of devices.
 13. The method of claim12, wherein the display comprises information associated with apercentile of the status of the component with respect to theinformation derived from the data received from the pool of devices. 14.The method of claim 1, further comprising: generating an alertconfigured to be provided to other entities based on the status, whereinthe alert comprises the status.
 15. The method of claim 1, wherein thecomponent comprises an optical sensor, and wherein the action comprisescapturing an image of an object.
 16. The method of claim 15, whereincomparing the information with the calibration data comprises comparingthe image with other images of the object previously captured by theoptical sensor.
 17. The method of claim 1, wherein the component is anactuator, and wherein the action comprises a predetermined movement ofthe actuator.
 18. A non-transitory computer readable memory havingstored therein instructions executable by one or more computing devicesto cause the one or more computing devices to perform functionscomprising: triggering an action of a component of a device;responsively receiving information associated with the action of thecomponent of the device; comparing the received information withcalibration data comprising information derived from data received froma pool of one or more devices utilizing same or similar components,wherein the data received from the pool comprises information associatedwith respective actions of respective components of the one or moredevices of the pool; and determining a status of the component based onthe comparison, wherein the status comprises information associated witha performance of the component.
 19. The computer readable memory ofclaim 18, wherein the information derived from data received from a poolof devices utilizing same or similar components comprises real-time datafrom the pool of devices, and further comprising instructions executableby the computing device to perform functions comprising: modifying thecalibration data based on the real-time data from the pool of devices.20. The computer readable memory of claim 18, further comprisinginstructions executable by the computing device to perform functionscomprising: generating an alert configured to be provided to otherentities based on the status, wherein the alert comprises the status.21. A system comprising: a device, configured to: receive via a networkinformation associated with instructions for performing an action of acomponent of the device; and in response to performing the action,transmit via the network information associated with the action of thecomponent received; and a computing component comprising a processor anda memory coupled to the processor and capable of communicating with apool of one or more devices over the network, the computing componentconfigured to: trigger the action of the component of the device overtime; responsively receive information associated with the action of thecomponent of the device from the sensor; compare the receivedinformation with calibration data compromising information derived fromdata received from the pool of devices, wherein the pool of devicesutilize same or similar components, and wherein the data received fromthe pool comprises information associated with respective actions ofrespective components of the one or more devices of the pool; anddetermine a status of the component based on the comparison, wherein thestatus comprises information associated with a performance of thecomponent.
 22. The system of claim 21, wherein the computing componentis further configured to: provide a display of the status of thecomponent, wherein the display comprises information associated with thepool of devices.
 23. The system of claim 22, wherein the displaycomprises information associated with a percentile of the status of thecomponent with respect to the information derived from the data receivedfrom the pool of devices.